No-commitment branch and bound search for distributed constraint optimization

Chechetka, Anton; Sycara, Katia

doi:10.1145/1160633.1160900

Cited by 69 publications

(62 citation statements)

References 8 publications

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“…17 Figure 11 shows equivalent non-concurrent constraint checks (ENCCCs) (Chechetka & Sycara, 2006;Silaghi & Faltings, 2004;Meisels, Kaplansky, Razgon, & Zivan, 2002;Yokoo et al, 1992) for the problems at size 40 and density 30%. There the overall cost is studied for the case where the latency of messages decreases to lower ratios with respect to the cost for constraint checks (predicting behavior for local area networks and MIMD parallel computer systems with efficient message passing).…”

Section: Methodsmentioning

confidence: 99%

“…Other extensions seem possible by integrating additive branch and bound searches on DFS sub-trees, as proposed by (Chechetka & Sycara, 2006;Yeoh, Koenig, & Felner, 2007). This can be added to ADOPT-ing by maintaining solution-based nogoods as suggested in (Silaghi, 2002).…”

Section: Possible Extensionsmentioning

confidence: 99%

“…However, we also provide results computed in a scenario simulating random latency in communication. Sometimes, local computation time is also factored into the evaluation metric by weighting the computation associated with each constraint check as a fraction (e.g., between one tenth and one millionth) of the latency of a message (Yokoo et al, 1992;Silaghi & Faltings, 2004;Chechetka & Sycara, 2006). The current value of this fraction for the Internet is around one thousand, estimating approximately 10 −4 seconds/constraint-check and 0.5 seconds/message.…”

Section: Introductionmentioning

confidence: 99%

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ADOPT-ing: unifying asynchronous distributed optimization with asynchronous backtracking

Silaghi

Yokoo

2008

Auton Agent Multi-Agent Syst

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This article presents an asynchronous algorithm for solving Distributed Constraint Optimization problems (DCOPs). The proposed technique unifies asynchronous backtracking (ABT) and asynchronous distributed optimization (ADOPT) where valued nogoods enable more flexible reasoning and more opportunities for communication, leading to an important speed-up. While feedback can be sent in ADOPT by COST messages only to one predefined predecessor, our extension allows for sending such information to any relevant agent. The concept of valued nogood is an extension by Dago and Verfaille of the concept of classic nogood that associates the list of conflicting assignments with a cost and, optionally, with a set of references to culprit constraints.DCOPs have been shown to have very elegant distributed solutions, such as ADOPT, distributed asynchronous overlay (DisAO), or DPOP. These algorithms are typically tuned to minimize the longest causal chain of messages as a measure of how the algorithms will scale for systems with remote agents (with large latency in communication). ADOPT has the property of maintaining the initial distribution of the problem. To be efficient, ADOPT needs a preprocessing step consisting of computing a Depth-First Search (DFS) tree on the constraint graph. Valued nogoods allow for automatically detecting and exploiting the best DFS tree compatible with the current ordering. To exploit such DFS trees it is now sufficient to ensure that they exist. Also, the inference rules available for valued nogoods help to exploit schemes of communication where more feedback is sent to higher priority agents. Together they result in an order of magnitude improvement.

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Section: Methodsmentioning

confidence: 99%

Section: Possible Extensionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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ADOPT-ing: unifying asynchronous distributed optimization with asynchronous backtracking

Silaghi

Yokoo

2008

Auton Agent Multi-Agent Syst

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“…We see two alternatives for addressing this problem: (a) renounce linear memory guarantees, and use a caching scheme like for example in NCBB (Chechetka & Sycara, 2006): this would allow for a similar reusability as in M-DPOP, where previously computated utilities can be extracted from the cache instead of having to be recomputed. Alternatively, (b) one can devise a scheme where the previously computed best solution can be saved as a reference, and subsequently used as an approximation while solving the marginal problems.…”

Section: Using Adopt For Faithful Efficient Social Choicementioning

confidence: 99%

M-DPOP: Faithful Distributed Implementation of Efficient Social Choice Problems

Petcu¹,

Faltings²,

Parkes³

2008

jair

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In the efficient social choice problem, the goal is to assign values, subject to side constraints, to a set of variables to maximize the total utility across a population of agents, where each agent has private information about its utility function. In this paper we model the social choice problem as a distributed constraint optimization problem (DCOP), in which each agent can communicate with other agents that share an interest in one or more variables. Whereas existing DCOP algorithms can be easily manipulated by an agent, either by misreporting private information or deviating from the algorithm, we introduce M-DPOP, the first DCOP algorithm that provides a faithful distributed implementation for efficient social choice. This provides a concrete example of how the methods of mechanism design can be unified with those of distributed optimization. Faithfulness ensures that no agent can benefit by unilaterally deviating from any aspect of the protocol, neither informationrevelation, computation, nor communication, and whatever the private information of other agents. We allow for payments by agents to a central bank, which is the only central authority that we require. To achieve faithfulness, we carefully integrate the Vickrey-Clarke-Groves (VCG) mechanism with the DPOP algorithm, such that each agent is only asked to perform computation, report information, and send messages that is in its own best interest. Determining agent i's payment requires solving the social choice problem without agent i. Here, we present a method to reuse computation performed in solving the main problem in a way that is robust against manipulation by the excluded agent. Experimental results on structured problems show that as much as 87% of the computation required for solving the marginal problems can be avoided by re-use, providing very good scalability in the number of agents. On unstructured problems, we observe a sensitivity of M-DPOP to the density of the problem, and we show that reusability decreases from almost 100% for very sparse problems to around 20% for highly connected problems. We close with a discussion of the features of DCOP that enable faithful implementations in this problem, the challenge of reusing computation from the main problem to marginal problems in other algorithms such as ADOPT and OptAPO, and the prospect of methods to avoid the welfare loss that can occur because of the transfer of payments to the bank.

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“…An alternate framework, called distributed constraint optimization (DCOP) [12,18], considers a group of distributed agents which manipulate a set of variables such that the cost associated with a set of constraints over the variables is minimized. DCOP is NP-complete [4], and many DCOP algorithms rely on pre-constructed (global and static) tree structures, thereby failing to be robust against failures [18,19].…”

Section: Introductionmentioning

confidence: 99%

Fully Decentralized Task Swaps with Optimized Local Searching

Liu

Michael

Shell

2014

Robotics: Science and Systems X

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Abstract-Communication constraints dictated by hardware often require a multi-robot system to make decisions and take actions locally. Unfortunately, local knowledge may impose limits that run against global optimality in a decentralized optimization problem. This paper redesigns the task-swap mechanism recently introduced in an anytime assignment algorithm to tackle the problem of decentralized task allocation for large scale multirobot systems. We propose a fully decentralized approach that allows local search processes to execute concurrently while minimizing interactions amongst the processes, needing neither global broadcast nor a multi-hop communication protocol. The formulation is analyzed in a novel way using tools from group theory and the optimization duality theory to show that the convergence of local searching processes is related to a shortest path routing problem on a graph subject to the network topology. Simulation results show that this fully decentralized method converges quickly while sacrificing little optimality.

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No-commitment branch and bound search for distributed constraint optimization

Cited by 69 publications

References 8 publications

ADOPT-ing: unifying asynchronous distributed optimization with asynchronous backtracking

ADOPT-ing: unifying asynchronous distributed optimization with asynchronous backtracking

M-DPOP: Faithful Distributed Implementation of Efficient Social Choice Problems

Fully Decentralized Task Swaps with Optimized Local Searching

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